AI RESEARCH
Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study
arXiv CS.CV
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ArXi:2605.14845v1 Announce Type: new Recent advancements in Vision-Language Models (VLMs) have nstrated strong capabilities in general visual reasoning, yet their applicability to rigorous biometric tasks remains unexplored. This work presents an exploratory study evaluating the zero-shot performance of state-of-the-art VLMs (GPT-5.2 and Gemini 2.5 Pro) on the Signature Verification Challenge (SVC) benchmark. To enable visual processing, raw kinematic time-series are converted into static images, encoding pressure information into stroke opacity whenever available in the source data.